Benchmarking Quantum Architecture Search with Surrogate Assistance

• URL: http://arxiv.org/abs/2506.06762v1
• Date: Sat, 07 Jun 2025 11:11:04 GMT
• Title: Benchmarking Quantum Architecture Search with Surrogate Assistance
• Authors: Darya Martyniuk, Johannes Jung, Daniel Barta, Adrian Paschke,
• Abstract summary: We present SQuASH, the Surrogate Quantum Architecture Search Helper.<n>We present the methodology for creating a surrogate benchmark for QAS and demonstrate its capability to accelerate the execution and comparison of QAS methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of quantum algorithms and their practical applications currently relies heavily on the efficient design, compilation, and optimization of quantum circuits. In particular, parametrized quantum circuits (PQCs), which serve as the basis for variational quantum algorithms~(VQAs), demand carefully engineered architectures that balance performance with hardware constraints. Despite recent progress, identifying structural features of PQCs that enhance trainability, noise resilience, and overall algorithmic performance remains an active area of research. Addressing these challenges, quantum architecture search (QAS) aims to automate the design of problem-specific PQCs by systematically exploring circuit architectures to optimize algorithmic performance, often with varying degrees of consideration for hardware constraints. However, comparing QAS methods is challenging due to the absence of a unified benchmark evaluation pipeline, and the high resource demands. In this paper, we present SQuASH, the Surrogate Quantum Architecture Search Helper, a benchmark that leverages surrogate models to enable uniform comparison of QAS methods and considerably accelerate their evaluation. We present the methodology for creating a surrogate benchmark for QAS and demonstrate its capability to accelerate the execution and comparison of QAS methods. Additionally, we provide the code required to integrate SQuASH into custom QAS methods, enabling not only benchmarking but also the use of surrogate models for rapid prototyping. We further release the dataset used to train the surrogate models, facilitating reproducibility and further research.

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